The advent of automated, quantitative turbulence reports from commercial aircraft has made it possible to use machine learning techniques to help develop such diagnostics. This paper describes the use of random forests--collections of weakly-correlated decision trees--to help establish relationships between storm features and aircraft turbulence that were then used to develop a fuzzy logic predictive algorithm for turbulence intensity near thunderstorms. Values from the RUC model were interpolated to the aircraft position, and a spatial “dartboard” oriented relative to the mean wind direction was used to collect data on storm intensity and coverage in a number of regions surrounding the turbulence measurement point. After random forests were trained to learn a predictive algorithm based on these quantities, its behavior was analyzed and a fuzzy logic algorithm was created to perform the turbulence diagnosis in real time. The fuzzy logic predictive algorithm was tuned based on the random forest and then verified using an independent testing set.